THE FORECASTING OF MONTHLY INFLATION IN MALANG CITY USING AN AUTOREGRESSIVE INTEGRATED MOVING AVERAGE

Eni Farida, Mohamad As'ad

Abstract

Abstract: Malang is known as a student city since there are a lot of schools and universities that can be found in Malang Indonesia. Malang is also an attractive tourist place with many tourist attractions in the city of Malang. Public transportation in the city of Malang is also very varied, ranging from conventional and based online. Access to the city of Malang is varied, namely trains, buses, and planes. Thus economic growth in the city of Malang is getting better, this can be seen from the economic activity in the increasingly crowded city of Malang. A good economy is usually followed by stable inflation. For this reason, it is necessary to examine how the monthly inflation rate in Malang city. This study aims to forecast inflation in the coming periods using the Autoregressive Integrated Moving Average (ARIMA) model. Secondary monthly inflation data is obtained from BPS Malang. From this research, the ARIMA model (2,0,3) is obtained. The accuracy model is used in this research namely root means square error (RMSE), mean absolute error (MAE), and mean absolute square error (MASE). The accuracy value is RMSE equal 0.2645467, MAE equal 0.2013898, and MASE equal 0.6047399.

Keywords: Monthly inflation forecasting, BPS Malang city, ARIMA model.

Full Text:

PDF

References

References

Abdurrahman. BMA., Ahmed AYA., & Abdellah AEY., (2018), Forecasting of Sudan Inflation Rates using ARIMA Model, International Journal of Economics and Financial, Vol. 8 no. 3, p.17-22.

As’ad, M. (2012), Finding the Best ARIMA Model to Forecast Daily Peak Electricity Demand, Proceedings of the Fifth Annual ASEARC Conference - Looking to the future - Programme and Proceedings, 2 - 3 February 2012, University of Wollongong, Australia.

Bawono, A. (2019), Factors Influencing The Inflation Of Indonesia In Islamic Perspectives, Jurnal Ilmiah Ekonomi Islam, Vol. 5 No. 2 p.81-88

Boediono (2001), Ekonomi Makro, BPFE, Yogyakarta.

Bollerslev, T., Engle, RF., & Nelson DB. (1994), Hand Book Econometrics-Chapter 49, Elsevier Science B.V. All rights reserved.

Distsarki, C., (2018), The Performance of Hybrid ARIMA-GARCH Modeling and Forecasting Oil Price, International Journal of Energy Economics and Policy, Vol 8(3), p.14-21.

Gujarati, D.N. 2009. Basics Econometrics, McGraw-Hill Irwin, Boston England.

https://www.bps.go.id/subject/3/inflasi.html#subjekViewTab1. Accessed on 11 January 2021.

Iqbal. M., & Naveed. A., (2016), Forecasting Inflation: Autoregressive Integrated Moving Average Model, European Scientific Journal, vol. 12, no. 1.

Jere, S., & Sianga, M., (2016), Forecasting Inflation Rate of Zambia Using Holt’s Exponential Smoothing, Open Journal of Statistics, vol. 6, p.363-372

Osarumwense, OI., & Waziri, EI., (2016). Modeling Monthly Inflation Rate Volatility, using Generalized Autoregressive Conditionally Heteroscedastic (GARCH) models: Evidence from Nigeria, Australian Journal of Basic and Applied Sciences, Vol.7(7): p.991-998.

Popoola, OP., Ayanrinde AW., Rafiu AA., & Oddusina MT., (2017), Time Series Analysis to Model and Forecasting Inflation Rate in Nigeria, Anale. Seria Informatica. Vol. XV fasc. 1.

Uwilingiyimana, C., Mung’tu J., & Harerimana, JDD., (2016), Forecasting Inflation in Kenya Using Arima - Garch Models, International Journal of Management and Commerce Innovations, Vol. 3, Issue 2, p. 15-27.

Wei, W.W.S., 1990. Time-series Analysis: Univariate and Multivariate Methods. Addison-Wesley Publishing Co., USA.

Refbacks

  • There are currently no refbacks.